Why I Run LLMs on My Own Hardware

The Setup

Placeholder content. Real post incoming.

Running large language models on your own hardware isn't about being contrarian. It's about control, economics, and capability convergence.

The Economics

When you run inference locally, the marginal cost of a query approaches zero after hardware amortization. Cloud API pricing is designed for occasional use. If AI is your core operating layer, the math changes fast.

A single NVIDIA RTX 4090 can run a 70B parameter model at reasonable speeds. Two of them, and you're in territory that would cost hundreds of dollars per day on cloud inference APIs.

The Capability Case

Local models have caught up faster than most people realize. Open-weight models from Meta, Mistral, and others now match or exceed GPT-3.5 on most benchmarks. For structured tasks, code generation, and domain-specific work, fine-tuned local models often outperform general-purpose cloud APIs.

The gap is closing. And for many production workloads, it's already closed.

The Sovereignty Case

Every query to a cloud API is a dependency. On uptime. On pricing. On terms of service. On data handling policies that can change without notice.

When you run locally, you own the entire stack. No rate limits. No content filtering surprises. No vendor deciding your use case violates their acceptable use policy.

This isn't paranoia. It's operational hygiene.

The Convergence

These three vectors are converging. Hardware is getting cheaper. Models are getting smaller and better. And the operational overhead of self-hosting is dropping as tooling matures.

The question isn't whether local-first AI infrastructure makes sense. It's when it becomes the default for anyone serious about building on top of AI.

We think that time is now.

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